Emotion Recognition in Intelligent Tutoring Systems for Android-Based Mobile Devices

  • Ramón Zatarain-Cabada
  • María Lucía Barrón-Estrada
  • Giner Alor-Hernández
  • Carlos A. Reyes-García
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8856)


In this paper, we present a Web-based system aimed at learning basic mathematics. The Web-based system includes different components like a social network for learning, an intelligent tutoring system and an emotion recognizer. We have developed the system with the goal of being accessed from any kind of computer platform and Android-based mobile device. We have also built a neural-fuzzy system for the identification of student emotions and a fuzzy system for tracking student´s pedagogical states. We carried out different experiments with the emotion recognizer where we obtained a success rate of 96%. Furthermore, the system (including the social network and the intelligent tutoring system) was tested with real students and the obtained results were very satisfying.


Intelligent Tutoring Systems Affective Computing Social Intelligence Artificial Neural Networks Mobile learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    O’Reilly, T. What is Web 2.0 (2005),
  2. 2.
    Hage, H., Aïmeur, E.: Harnessing Learner’s Collective Intelligence: A Web2.0 Approach to E-Learning. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 438–447. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Boyd, D., Ellison, N.B.: Social network sites: Definition, history and scholarship. Journal of Computer-Mediated Communication 13(1), article 11 (2007),
  4. 4.
    Picard, R.W.: Affective Computing. M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 321 (1995)Google Scholar
  5. 5.
    Arroyo, I., Woolf, B., Cooper, D., Burleson, W., Muldner, K., Christopherson, R.: Emotions sensors go to school. In: Proceedings of the 14th International Conference on Artificial Intelligence in Education, pp. 17–24. IOS Press, Amsterdam (2009)Google Scholar
  6. 6.
    Calvo, R.A., D’Mello, S.: Affect Detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affect Computing 1, 18–37 (2010)CrossRefGoogle Scholar
  7. 7.
    Baker, R.S.J.D., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be Frustrated than Bored: The Incidence, Persistence, and Impact of learners’ Cognitive-affective States During Interactions with three Different Computer-Based Learning Environments. International Journal of Human-Computer Studies 68(4), 223–241 (2010)CrossRefGoogle Scholar
  8. 8.
    Sabourin, J., Rowe, J.P., Mott, B.W., Lester, J.C.: When Off-Task is On-Task: The Affective Role of Off-Task Behavior in Narrative-Centered Learning Environments. In: Biswas, G., Bull, S., Kay, J., Mitrovic, A. (eds.) AIED 2011. LNCS, vol. 6738, pp. 534–536. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gardner, L., Sheridan, D., White, D.: AWeb-based learning and assessment system to support flexible education. Journal of Computer Assisted Learning 18, 125–136 (2002)CrossRefGoogle Scholar
  10. 10.
    Costa, D.S.J., Mullan, B.A., Kothe, E.J., Butow, P.: A web-based formative assessment tool for Masters students: a pilot study. Computers & Education 54(4), 1248–1253 (2010)CrossRefGoogle Scholar
  11. 11.
    Chen, G.D., Chang, C.K., Wang, C.Y.: Ubiquitous learning website: scaffold learners by mobile devices with information-aware techniques. Computers & Education 50, 77–90 (2008)CrossRefGoogle Scholar
  12. 12.
    Nixon, M., Aguado, A.: Feature Extraction & Image Processing, 2nd edn. Academic Press (2008)Google Scholar
  13. 13.
    Doignon, J.-P., Falmagne, J.C.: Knowledge Spaces. Springer (1999)Google Scholar
  14. 14.
    Ekman, P., Oster, H.: Facial expressions of emotion. Annual Review of Psychology 30, 527–554 (1979)CrossRefGoogle Scholar
  15. 15.
    Weka Oficial Homepage. University of Waikato, New Zealand,
  16. 16.
    Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D., Hawk, S., van Knippenberg, A.: Presentation and validation of the Radboud Faces Database. Cognition & Emotion 24(8), 1377–1388 (2010), doi:10.1080/02699930903485076CrossRefGoogle Scholar
  17. 17.
    Ainsworth, S.: Evaluation methods for learning environments (2005), Tutorial at AIED 2005 available at

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ramón Zatarain-Cabada
    • 1
  • María Lucía Barrón-Estrada
    • 1
  • Giner Alor-Hernández
    • 2
  • Carlos A. Reyes-García
    • 3
  1. 1.Instituto Tecnológico de CuliacánCuliacánMéxico
  2. 2.Instituto Tecnológico de OrizabaOrizabaMéxico
  3. 3.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Sta. Ma. TonanzintlaMéxico

Personalised recommendations